A Robust algorithm for Run-to-run Optimization of Batch Processes

A standard run-to-run model-based optimization approach consists of updating the model after every run and, then re-optimizing for the next run with the updated model. However, in the presence of model structure error, the convergence of this two-step approach to the true plant optimum cannot be guaranteed. This paper presents an alternative approach where a correction term is added to the model outputs such that the new updated model parameters simultaneously satisfy the identification and optimization objectives. The effect of parametric uncertainty on optimization results is considered explicitly by minimizing the expected value of the cost function. For computational efficiency, the uncertainty propagation step is performed using Polynomial Chaos expansions. The methodology is illustrated using a fed-batch process for penicillin production. When compared to the standard two-step approach, the proposed methodology provides significant increase in the amount of penicillin.